Statistical Analysis of the Number of Household Vehicles Used for Hurricane Ivan Evacuation
Publication: Journal of Transportation Engineering
Volume 140, Issue 12
Abstract
The objectives of this paper are to identify the contributing factors to households’ choice of the number of vehicles used for evacuation and to develop predictive models of this choice that explicitly consider the constraint imposed by the number of vehicles owned by the household. This constraint is not accommodated by regular ordered response logit models. Data comes from a poststorm survey for Hurricane Ivan. Two models that are variants of the regular Poisson regression model are developed: a Poisson model with exposure and right-censored Poisson regression. The right-censored Poisson model is preferred due to its inherent capabilities, better fit to the data, and superior predictive power. The model and individual variable analyses indicate that households traveling longer distances or evacuating later are more likely to use fewer vehicles. Households with prior hurricane experience and pet owners are more likely to use a greater number of vehicles. Income and distance from the coast are insignificant in the multivariable models, although the individual effect of distance from the coast has a statistically significant bivariate relationship with vehicle usage choice based on the Pearson correlation measure. A method for using the right-censored Poisson model to produce the desired share of vehicle usage is also discussed for generating individual predictions for hurricane evacuation demand simulation.
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Acknowledgments
This research was supported by National Science Foundation Award SES-0826873 for which the authors are grateful. The Hurricane Ivan survey was supported by the USACE (http://chps.sam.usace.army.mil/USHESdata/Assessments/2004Storms/Ivan/Ivan_Frame.htm [go to Behavioral Data]) and directed by Betty Morrow and Hugh Gladwin. The authors also thank Dr. William Greene and Dr. Kris Wernstedt for their advice on the Poisson regression models. However, the authors are solely responsible for the findings in this document.
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Published In
Journal of Transportation Engineering
Volume 140 • Issue 12 • December 2014
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Dec 18, 2013
Accepted: May 2, 2014
Published online: Jul 11, 2014
Published in print: Dec 1, 2014
Discussion open until: Dec 11, 2014
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